global_local/models/ImageBind/README.md

# ImageBind: One Embedding Space To Bind Them All

**[FAIR, Meta AI](https://ai.facebook.com/research/)** 

Rohit Girdhar*,
Alaaeldin El-Nouby*,
Zhuang Liu,
Mannat Singh,
Kalyan Vasudev Alwala,
Armand Joulin,
Ishan Misra*

To appear at CVPR 2023 (*Highlighted paper*)

[[`Paper`](https://facebookresearch.github.io/ImageBind/paper)] [[`Blog`](https://ai.facebook.com/blog/imagebind-six-modalities-binding-ai/)] [[`Demo`](https://imagebind.metademolab.com/)] [[`Supplementary Video`](https://dl.fbaipublicfiles.com/imagebind/imagebind_video.mp4)] [[`BibTex`](#citing-imagebind)]

PyTorch implementation and pretrained models for ImageBind. For details, see the paper: **[ImageBind: One Embedding Space To Bind Them All](https://facebookresearch.github.io/ImageBind/paper)**.

ImageBind learns a joint embedding across six different modalities - images, text, audio, depth, thermal, and IMU data. It enables novel emergent applications ‘out-of-the-box’ including cross-modal retrieval, composing modalities with arithmetic, cross-modal detection and generation.



![ImageBind](https://user-images.githubusercontent.com/8495451/236859695-ffa13364-3e39-4d99-a8da-fbfab17f9a6b.gif)

## ImageBind model

Emergent zero-shot classification performance.

<table style="margin: auto">
  <tr>
    <th>Model</th>
    <th><span style="color:blue">IN1k</span></th>
    <th><span style="color:purple">K400</span></th>
    <th><span style="color:green">NYU-D</span></th>
    <th><span style="color:LightBlue">ESC</span></th>
    <th><span style="color:orange">LLVIP</span></th>
    <th><span style="color:purple">Ego4D</span></th>
    <th>download</th>
  </tr>
  <tr>
    <td>imagebind_huge</td>
    <td align="right">77.7</td>
    <td align="right">50.0</td>
    <td align="right">54.0</td>
    <td align="right">66.9</td>
    <td align="right">63.4</td>
    <td align="right">25.0</td>
    <td><a href="https://dl.fbaipublicfiles.com/imagebind/imagebind_huge.pth">checkpoint</a></td>
  </tr>
  
</table>

## Usage

Install pytorch 1.13+ and other 3rd party dependencies.

```shell
conda create --name imagebind python=3.8 -y
conda activate imagebind

pip install -r requirements.txt
```

For windows users, you might need to install `soundfile` for reading/writing audio files. (Thanks @congyue1977)

```
pip install soundfile
```


Extract and compare features across modalities (e.g. Image, Text and Audio).

```python
import data
import torch
from models import imagebind_model
from models.imagebind_model import ModalityType

text_list=["A dog.", "A car", "A bird"]
image_paths=[".assets/dog_image.jpg", ".assets/car_image.jpg", ".assets/bird_image.jpg"]
audio_paths=[".assets/dog_audio.wav", ".assets/car_audio.wav", ".assets/bird_audio.wav"]

device = "cuda:0" if torch.cuda.is_available() else "cpu"

# Instantiate model
model = imagebind_model.imagebind_huge(pretrained=True)
model.eval()
model.to(device)

# Load data
inputs = {
    ModalityType.TEXT: data.load_and_transform_text(text_list, device),
    ModalityType.VISION: data.load_and_transform_vision_data(image_paths, device),
    ModalityType.AUDIO: data.load_and_transform_audio_data(audio_paths, device),
}

with torch.no_grad():
    embeddings = model(inputs)

print(
    "Vision x Text: ",
    torch.softmax(embeddings[ModalityType.VISION] @ embeddings[ModalityType.TEXT].T, dim=-1),
)
print(
    "Audio x Text: ",
    torch.softmax(embeddings[ModalityType.AUDIO] @ embeddings[ModalityType.TEXT].T, dim=-1),
)
print(
    "Vision x Audio: ",
    torch.softmax(embeddings[ModalityType.VISION] @ embeddings[ModalityType.AUDIO].T, dim=-1),
)

# Expected output:
#
# Vision x Text:
# tensor([[9.9761e-01, 2.3694e-03, 1.8612e-05],
#         [3.3836e-05, 9.9994e-01, 2.4118e-05],
#         [4.7997e-05, 1.3496e-02, 9.8646e-01]])
#
# Audio x Text:
# tensor([[1., 0., 0.],
#         [0., 1., 0.],
#         [0., 0., 1.]])
#
# Vision x Audio:
# tensor([[0.8070, 0.1088, 0.0842],
#         [0.1036, 0.7884, 0.1079],
#         [0.0018, 0.0022, 0.9960]])

```

## Model card
Please see the [model card](model_card.md) for details.

## License

ImageBind code and model weights are released under the CC-BY-NC 4.0 license. See [LICENSE](LICENSE) for additional details.

## Contributing

See [contributing](CONTRIBUTING.md) and the [code of conduct](CODE_OF_CONDUCT.md).

## Citing ImageBind

If you find this repository useful, please consider giving a star :star: and citation

```
@inproceedings{girdhar2023imagebind,
  title={ImageBind: One Embedding Space To Bind Them All},
  author={Girdhar, Rohit and El-Nouby, Alaaeldin and Liu, Zhuang
and Singh, Mannat and Alwala, Kalyan Vasudev and Joulin, Armand and Misra, Ishan},
  booktitle={CVPR},
  year={2023}
}
```